Henrik Myhre Jensen is a Professor at the College of Engineering , Aarhus University, specializing in Mechanics of Materials , Solid Mechanics , and Mechanical Engineering . His research focuses on fracture mechanics, composite materials, and computational modeling of structural behaviors. Research Focus Fracture mechanics in composites and layered materials Computational modeling of kink band propagation Surface wear and coating technologies Ultrasound imaging applications in mechanical systems Notable Contributions Henrik has contributed to understanding crack propagation in cantilever beams, developed numerical methods for simulating delamination in composites, and explored buckling instabilities in solids. His recent work connects machine learning (holomorphic neural networks) to traditional fracture mechanics problems. Key Projects MAGFLY (2017-2021): Magnets for Flywheel Energy Storage InnoVacc (2009): Pressure Testing of Vacuum Chambers Simulation of composite structures (2011-2020): Micro-mechanical modeling
Professor Timothy Walsh serves as Chair of Critical Care at the University of Edinburgh's Usher Institute within the College of Medicine and Veterinary Medicine. He concurrently holds the position of Director of Innovation for NHS Lothian and Health Innovation South East Scotland, bridging academic research with clinical implementation. His dual roles position him at the forefront of critical care research and healthcare innovation in the UK. Walsh's research spans critical and perioperative care, with a programmatic approach building complex multi-center trials. His work integrates epidemiology, systematic reviews, cohort studies, and stakeholder engagement to develop pragmatic trials. Recent focus includes AI algorithm validation, sedation protocols, transfusion medicine, and sepsis management. His fingerprint reveals deep expertise in Intensive Care Medicine (100%), Intensive Care Unit operations (74%), and Critical Illness (70%), with notable contributions to sedation research (35%) and sepsis (26%). His 221 research outputs include high-impact publications in NEJM, JAMA, and The Lancet. Current projects like the SHORTER antibiotic trial and aerosolized virus quantification study demonstrate ongoing leadership in trial methodology. As Director of Innovation for NHS Lothian (2018-2024), he established data-driven innovation frameworks connecting academic and industry partners to address NHS challenges. Walsh has secured £9 million as Chief Investigator and £34 million as co-applicant from NIHR, MRC, Wellcome, and industry sources. His leadership extends to founding the NIHR critical care specialty group (2007-15) and UK critical care research group (2007-16), which remain foundational to UK critical care research infrastructure. Trustee at Chest Heart & Stroke Scotland (2021-present) Director of Research & Development for NHS Lothian (2017-2021) Chair of 19 trial steering/data safety monitoring committees Leadership in 13 ECTU trials, 9 UK trials, and multiple international studies
Ehsan Modiri is a researcher at the Department of Hydrosystem Modelling , Helmholtz Centre for Environmental Research (UFZ), Germany. His work focuses on climate change impacts on hydrological systems, drought monitoring, and environmental modeling using advanced computational frameworks. Affiliation: UFZ - Helmholtz Centre for Environmental Research Department: Hydrosystem Modelling Research Themes: Climate Change, Droughts, Hydrological Forecasting, Water Resource Management Research Interests: Modiri specializes in understanding hydrological responses to climate change, particularly in drought dynamics and soil moisture variability. His work bridges observational data with sophisticated modeling frameworks to improve predictability of water balance components under warming scenarios. Scientific Contributions: Recent publications highlight his role in developing high-resolution drought simulations, evaluating hydrological model performance, and analyzing groundwater responses to global warming. He participates in large-scale European hydrological projects and collaborates on climate-hydrology integration initiatives.
Dr. Srishti Banerji is an Assistant Professor in the Department of Civil and Environmental Engineering at Utah State University and Director of the Systems, Materials, and Structural Health (SMASH) Lab. She leads research on advanced construction materials, structural resilience under extreme loads (particularly fire), sustainable infrastructure, and structural health monitoring. Her group focuses on experimental testing, numerical simulations, and developing design solutions for civil infrastructure. Education: PhD in Civil (Structural) Engineering, Michigan State University (2021) MS in Civil (Structural) Engineering, Concordia University (2016) BS in Civil Engineering, National Institute of Technology Silchar (2013) Research Focus: Her work spans: 1) Characterization of high-performance/sustainable materials (e.g., UHPC, recycled glass pozzolan), 2) Structural behavior under fire exposure, 3) Integration of electric charging systems in concrete pavements, 4) Non-destructive testing and structural health monitoring, and 5) Retrofitting techniques for infrastructure strengthening. She employs machine learning, thermo-mechanical modeling, and full-scale experimentation. Publication Trends: Her 13+ journal articles primarily analyze fire resistance of concrete/timber structures, UHPC material properties at high temperatures, sensor-based infrastructure monitoring, and sustainable material development. Recent works increasingly incorporate machine learning and electrification concepts. Awards & Honors: Teacher of the Year (USU, 2025) ASCE ExCEEd Faculty Teaching Fellowship (2023) Top Cited Article Award, Fire and Materials Journal (2023) SHMII-11 Early Career Grant (2022) NSERC Scholarship (2015) Best Conference Paper (SEC 2016) Current Projects & Teams: She leads 5+ funded projects including fire performance of polymer concrete, self-healing concrete for bridges, and Utah-sourced UHPC development. Mentees include 3 PhD students (Abdullah Al Sarfin, Mehrnoosh Nazari, Mahmoud Ali) and alumni working on sustainable materials and additive manufacturing.
Associate Professor Abdul Ihdayhid is a Research Leader in Cardiovascular Biology at the Curtin Medical School , Curtin University, within the Faculty of Health Sciences. His work focuses on advanced cardiac imaging techniques, particularly coronary CT angiography, fractional flow reserve modeling, and AI integration in cardiovascular diagnostics. Key Research Areas: Cardiovascular imaging, artificial intelligence applications, aortic stenosis interventions, and ethical implications of AI in medicine. Recent Publications: Analysis of high-risk coronary plaque, telehealth adaptations during pandemics, and AI-driven CAC scoring innovations. Collaborations: Extensive partnerships with institutions across Australia and New Zealand on multicenter studies like the Australian-New Zealand SCAD cohort. His 2024-2025 work emphasizes machine learning for plaque quantification and ethical frameworks in AI implementation. Email: Abdul.Ihdayhid@curtin.edu.au
Dr. Alexander Breuss is part of the Sensory-Motor Systems Professorship at ETH Zürich, focusing on developing innovative robotic and sensor technologies for medical applications, particularly in sleep disorder treatment and home healthcare. His work integrates biomedical engineering, robotics, and machine learning to address challenges in sleep medicine and cardiovascular diagnostics. Key projects include the Somnomat Care robotic bed for vestibular stimulation and the Somnomat Casa system for nocturnal interventions. His research spans sensorized devices for sleep monitoring, clinical trials for rhythmic movement disorders, and cardiovascular disease prognosis using imaging and hemodynamic analysis. Dr. Breuss collaborates on interdisciplinary projects, combining engineering and clinical insights to advance healthcare technologies. His research interests include the design of medical devices for home environments, non-invasive monitoring systems, and closed-loop robotic systems for therapeutic applications. Notable contributions include lightweight wearable sensors for movement disorders and automated sleep position classification using neural networks. He has published extensively on topics such as pleural effusion in aortic stenosis and ECG-based cardiac prognosis, highlighting his cross-disciplinary approach to biomedical challenges. No scientific awards are explicitly mentioned for Dr. Breuss. His work is centered at the Sensory-Motor Systems Lab, where he contributes to advancing technologies that improve patient care and sleep quality through robotics and sensor innovation.
Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.
Professor Tony Jan leads the Centre for Artificial Intelligence Research and Optimisation (AIRO) at Torrens University Australia's Design and Creative Technology school. He holds a PhD in Computing Science from the University of Technology Sydney (2004) and a Bachelor of Engineering from the University of Western Australia (1999). His research focuses on federated machine learning for IoT security, ensembled machine learning for real-time applications, cognitive machines for human-centric computing, and smart sensor networks for healthcare and security. He has secured ARC grants and industry partnerships with NVIDIA, IBM, and Microsoft. Awards include the 2024 SEI Global Academic Excellence Award and the 2023 Torrens University Excellence Award. Research collaborations span global partners, with contributions to UN Sustainable Development Goals in education and industry. His work bridges academia and industry, expanding AI program enrollments by 2,000+ students and enhancing student satisfaction by 15%. He advises PhD students on topics like IIoT cybersecurity and smart cities, and has produced over 97 publications since 1999. Education: PhD (UTS, 2004), BEng (UWA, 1999) Research Themes: AI for Industry 5.0, Cybersecurity, Smart Cities, Healthcare Technology Key Partnerships: NVIDIA, CIMIC, Palo Alto Networks Recent Projects: Federated learning for health IoT, drone vision intelligence, ransomware detection His work emphasizes ethical AI adoption in design and healthcare, with publications exploring AI ethics, generative AI applications, and sustainable technology integration.
Dirk Repsilber is a Professor of Medical Science at Örebro University, specializing in functional bioinformatics. He leads research on molecular patterns in patients to improve diagnosis and treatment. Born in Lübeck, Germany, he earned his PhD in ecological genetics from the University of Hamburg and held postdoctoral roles at institutions across Sweden and Germany. His work integrates bioinformatics, biostatistics, and clinical collaboration to address complex biological systems. Education: PhD in Ecological Genetics (University of Hamburg, 1999), postdoctoral training at Uppsala University and SLU, followed by roles at German institutions including Lübeck, Potsdam, Rostock, and Örebro. Research Interests: Molecular networks, biosignature development, systems biology approaches to genotype-phenotype mapping, and interdisciplinary collaborations in medicine, statistics, and computer science. Key Projects: BIO IBD (biomarker discovery in inflammatory bowel disease), Cell Painting for toxicity assessment, and systems-level immunomonitoring in pediatric oncology. Active in cross-disciplinary teams like the Center for Life Sciences Nutrition-Gut-Brain Interactions. Teaching & Supervision: Cross-disciplinary courses in bioinformatics, statistical counseling, and supervision of PhD students in diverse fields. Emphasizes problem-based learning and communication skills across disciplines.
Dr. Fatemeh Golpayegani is an Assistant Professor at the School of Computer Science, University College Dublin. She leads the Multi-agent Systems and Sustainable Solutions lab (MAS3.ucd.ie) and has secured over €1.5M in research grants. Her academic roles include BSc Stage 4 Coordinator and Chair of Women@CS (2012–2023). She holds a PhD from Trinity College Dublin (2018) and professional qualifications in university teaching from UCD. Education: PhD in Computer Science, Trinity College Dublin (2018) Professional Diploma in University Teaching & Learning, University College Dublin (2024) Professional Certificate in University Teaching & Learning, University College Dublin (2023) Research Interests: Focuses on multi-agent systems, sustainability, intelligent transport systems, autonomous decision-making, and edge computing. Her work integrates reinforcement learning, ontology-based models, and adaptive systems to address challenges in smart cities, energy grids, and infrastructure monitoring. Grants & Projects: Principal Investigator for the EU-funded RE-ROUTE project (€multi-million, 2023–2026) on intelligent transport networks. Co-Principal Investigator for the Augmented CCAM project on connected/cooperative autonomous mobility. Funded investigator in SFI centres (I-Form, CONNECT, Biorbic). Awards & Recognition: Researcher of the Year Award (2022) Member of Young Academy of Ireland (2023) Teaching & Mentoring: Coordinates modules in algorithms, Java programming, and operating systems. Supervises PhD students in SFI centres and mentors postdoctoral researchers. Active in promoting EDI as Chair of d-real doctoral training centre. Labs & Collaborations: Leads the MAS3 lab, collaborating on projects like CAPTAIN CARBON (sustainable transport gamification) and ontology-enhanced traffic signal control systems.
Jalal Al-Tamimi is an Associate Professor (Maître de conférences) in experimental phonetics and phonology at Université Paris Cité, affiliated with the Laboratoire de Linguistique Formelle (LLF). He holds an HDR (Habilitation à Diriger des Recherches) in Phonetics and Phonology and has extensive experience in teaching and research across institutions including Newcastle University, UK. His research focuses on the interface between phonetics and phonology, particularly the role of acoustic and articulatory correlates in speech production, perception, and acquisition. He specializes in Arabic dialects, using advanced quantitative methods like generalized additive mixed models and machine learning techniques. Al-Tamimi’s academic journey includes a PhD from Lyon 2 University (2007) and postdoctoral roles in the UK before joining Paris. He currently directs the Master in Language Sciences and co-leads research strands on phonetic complexity and language variation. His administrative roles include membership in the AFCP and coordination of experimental linguistics at LLF. Research interests span laboratory phonology, articulatory-acoustic mapping, and the application of automated methods in clinical diagnosis. His work explores epilarynx dynamics in Arabic gutturals and forced-alignment systems for dialectal Arabic. Awards include the Peter Ladefoged Prize (2018) and Leverhulme Fellowships. He supervises numerous PhD and MA students across computational linguistics and phonetics. Publications highlight contributions to vowel dynamics, speech processing in Alzheimer’s, and cross-linguistic phonological studies. His lab collaborations involve the MAUS team and Sorbonne’s SCAI, focusing on speech technology and pathological speech analysis.
Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.
Dr. Jennifer Ahjin Kim is an Assistant Professor in the Department of Neurology at Yale School of Medicine. She specializes in neurocritical care, focusing on quantitative analysis of electroencephalography (EEG) and neuroimaging for early diagnosis and treatment optimization in patients with severe neurologic injuries. Her clinical interests include traumatic brain injury, subarachnoid hemorrhage, stroke, and post-traumatic epilepsy. PhD in Neuroscience, Brown University (2012) MD from Brown University (2012) Neurology Residency, Massachusetts General Hospital/Brigham & Women's Hospital (2016) Neurocritical Care Fellowship, Massachusetts General Hospital/Brigham & Women's Hospital (2019) Dr. Kim’s research integrates multimodal data (EEG, MRI, CT) with machine learning to predict secondary complications after brain injuries. She actively contributes to clinical trials like BOOST3 and ASPIRE, aiming to improve outcomes for patients with traumatic brain injury, stroke, and hemorrhage. Her recent work emphasizes automated detection of epileptiform discharges, predictive modeling for delayed cerebral ischemia, and application of NLP to CT reports. Collaborations include frequent partnerships with Guido Falcone, Lawrence Hirsch, and Emily Gilmore. Dr. Kim leads the Kim Laboratory, which focuses on bedside monitoring technologies and secondary prevention strategies.
Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.
Daniel Berthe is a Researcher affiliated with the Department of Physics at the Technical University of Munich , operating under the Faculty of Medicine . His work focuses on advanced X-ray imaging techniques such as grating-based phase-contrast and spectral X-ray imaging , with applications in medical diagnostics and dentistry . Specializes in photon counting detectors and dark-field imaging Collaborates with institutions like the Munich Institute of Biomedical Engineering Develops simulation frameworks for improving panoramic dental imaging His research spans breast-CT , mammography , and bone mineral density estimation , leveraging grating-based X-ray phase-contrast for enhanced tissue visualization. Recent publications highlight his contributions to non-destructive testing in industrial contexts and self-supervised denoising algorithms in imaging. Presentations include talks at the SpecXray 2024 conference in Geneva and the IMXP 2023 in Munich on spectral X-ray applications in dental imaging.
